Feast Stores
The GridGain Feast integration provides a way of using GridGain as online store for Feast, providing high-performance, in-memory data storage and retrieval for feature serving.
Features
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GridGain Integration: Leverages GridGain’s in-memory database to provide online features for real-time model predictions.
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Feature Management: Feast manages feature definitions, versioning, and the synchronization between online and offline stores.
Project Structure
The project consists of two main components:
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Ignite Online Store (
online_store.py): Sets up Apache Ignite as the online feature store. -
GridGain Online Store (
gridgain_online_store.py): Configures GridGain as the online feature store.
Both implementations provide similar functionality but are tailored to their respective systems.
Prerequisites
The following is required to use LangChain integration:
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GridGain 8.9.17 or later with an appropriate license is required to use vector store.
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Python 3.11.7 or later is required to use the LangChain extension.
Installation
Install the package using pip:
pip install feast-gridgainAPI Reference
Online Store Reference
Both Apache Ignite and GridGain stores provide the following methods:
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online_read(config, table, entity_keys, requested_features)- Reads feature values from the online store. -
online_write_batch(config, table, data, progress)- Writes a batch of feature data to the online store. -
update(config, tables_to_delete, tables_to_keep, entities_to_delete, entities_to_keep, partial)- Updates the online store based on changes to the feature repository. -
teardown(config, tables, entities)- Cleans up the online store.
Feast Tutorial
GridGain provides a comprehensive example of using Ignite for creating an online store for a Feast integration with Kafka. This tutorial provides a detailed implementation that demonstrates the integration of Ignite Online Store with Feast in a Continuous Glucose Monitoring (CGM) use case. It includes examples of configuration, feature definitions, and usage in different environments.
The full tutorial is available in the Low-Latency Machine Learning Feature Store with GridGain and Feast section.
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